Dynamic pricing incentive for participatory sensing

User participation is one of the most important elements in participatory sensing application for providing adequate level of service quality. However, incentive mechanism and its economic model for user participation have been less addressed so far in this research domain. This paper studies the economic model of user participation incentive in participatory sensing applications. To stimulate user participation, we design and evaluate a novel reverse auction based dynamic pricing incentive mechanism where users can sell their sensing data to a service provider with users’ claimed bid prices. The proposed incentive mechanism focuses on minimizing and stabilizing the incentive cost while maintaining adequate level of participants by preventing users from dropping out of participatory sensing applications. Compared with random selection based fixed pricing incentive mechanism, the proposed mechanism not only reduces the incentive cost for retaining the same number of participants but also improves the fairness of incentive distribution and social welfare. It also helps us to achieve the geographically balanced sensing measurements and, more importantly, can remove the burden of accurate price decision for user data that is the most difficult step in designing incentive mechanism.     

Social welfare maximization in participatory smartphone sensing

Participatory smartphone sensing has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application. Most existing studies consider that smartphone users are strictly strategic and completely rational, which try to maximize their own payoffs. A number of incentive mechanisms are designed to encourage smartphone users to participate, which can achieve only suboptimal system performance. However, few existing studies can maximize a system-wide objective which takes both the platform and smartphone users into account. This paper focuses on the crucial problem of maximizing the system-wide performance or social welfare for a participatory smartphone sensing system. There are two great challenges. First, the social welfare maximization cannot be realized on the platform side because the cost of each user is private and unknown to the platform in reality. Second, the participatory sensing system is a large-scale real-time system due to the huge number of smartphone users who are geo-distributed in the whole world. A price-based decomposition framework is proposed in our previous work (Liu and Zhu, 2013), in which the platform provides a unit price for the sensing time spent by each user and the users return the sensing time via maximizing the monetary reward. This pricing framework is an effective incentive mechanism as users are motivated to participate for monetary rewards from the platform. In this paper, we propose two distributed solutions, which protect users’ privacy and achieve optimal social welfare. The first solution is designed based on the Lagrangian dual decomposition. A poplar iterative gradient algorithm is used to converge to the optimal value. Moreover, this distributed method is interpreted by our pricing framework. In the second solution, we first equivalently convert the original problem to an optimal pricing problem. Then, a distributed solution under the pricing framework via an efficient price-updating algorithm is proposed. Experimental results show that both two distributed solutions can achieve the maximum social welfare of a participatory smartphone system.     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

An incentive compatible reputation mechanism for ubiquitous computing environments

The vision of ubiquitous computing is becoming a reality thanks to the advent of portable devices and the advances in wireless networking technologies. It aims to facilitate user tasks through seamless utilization of services available in the surrounding environments. In such distributed environments featuring openness, interactions such as service provision and consumption between entities that are unknown or barely known to each other, are commonplace. Trust management through reputation mechanism for facilitating such interactions is recognized as an important element of ubiquitous computing. It is, however, faced by the problems of how to stimulate reputation information sharing and enforce honest recommendation elicitation. We present in this paper an incentive compatible reputation mechanism to facilitate the trustworthiness evaluation of entities in ubiquitous computing environments. It is based on probability theory and supports reputation evolution and propagation. Our reputation mechanism not only shows robustness against lies, but also stimulates honest and active recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can obtain the most number of honest (helpful) recommendations and thus suffer the least number of wrong trust decisions, as validated by simulation based evaluation. The proposed reputation mechanism is also implemented as part of a QoS-aware Web service discovery middleware and evaluated regarding its overhead on service discovery latency.     

The impact of reputation on supply chains. An analysis of permanent and discounted reputation

In long-term recurring contractual relationships, which are common in the B2B-arena, reputation and trust play a crucial role. This analysis investigates the joint impact of reputation and price-based ranking of suppliers on the material flow in the supply chain. Positive reputation proves to be a key factor in reaching dominating market positions, which illustrates the importance of building brand awareness in all stages of a supply chain. Through our simulation, it will be observed that the ranking of suppliers by reputation-based choice has a stabilizing effect on the material flow in the supply chain. A strong reputation component in the individual choice stimulates the formation of monopolies, while the discount of reputation imposes a countertendency on this effect. The Bullwhip Effect, another phenomenon that carries a countertendency to the reputation-based monopoly effect, is observed to be even stronger for members of tiers with a high fluctuation of order rates.     

Towards decision support for participatory democracy

In many parts of the world there is growing demand for participation in public policy decision making. This demand could be satisfied by the design and deployment of Web-based group decision support systems to aid large groups of, possibly unsophisticated, users in participating in such decisions. After describing several mechanisms for participatory democracy, we provide a framework for decision support in this area and describe decision support functions that could be implemented in such a framework. We illustrate the ideas with a specific system to support participatory budget elaboration through the Web. Several practical issues are discussed along the way.

CellTrust: a reputation model for C2C commerce

The improvement of wireless technologies and the increasing spread of mobile phones open new possibilities to perform mobile Customer-to-Customer commercial activities. In this new scenario, where users cannot rely on stable connections, it assumes a great relevance how to trust the counterpart in a transaction and how to avoid that a disconnection, possible in wireless connections, can encourage users to cheat. To tackle these issues we propose a feedback-based reputation mechanism able to detect malicious users better than other state-of-the-art techniques, as shown by the large number of experiments run to measure the accuracy of the compared methods in the most common situations.

Collaborative reputation mechanisms for electronic marketplaces

Members of electronic communities are often unrelated to each other, they may have never met and have no information on each other's reputation. This kind of information is vital in Electronic Commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. Collaborative reputation mechanisms can provide personalized evaluations of the various ratings assigned to each user to predict their reliabilities. While these reputation mechanisms are developed in the context of electronic commerce, they are applicable in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.     

A weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks

Spectrum sensing plays a critical role in cognitive radio networks. A good sensing scheme can reduce the false alarm probability and the miss detection probability, and thus improves spectrum utilization. This paper presents a weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks, to increase the spectrum sensing accuracy. The framework contains two modules. In the first module, each cognitive radio performs local spectrum sensing and computes the total error probability, which combines the false alarm probability and the miss detection probability. The total error probability and the energy signal from the primary user are then sent to the base station. In the second module, the base station makes a final decision after combining the weighted energy signals from all cognitive radios. The final decision is then broadcasted back to all cognitive radios. To reduce the computation complexity and communication overhead, the base station also instructs the cognitive radios that have large total error probabilities not to report their local sensing results. We have developed a theoretical model for the proposed framework, and derived the optimal detection threshold, as well as the minimum number of cognitive radios required to participate in cooperative sensing, subject to a given total error probability. Numerical results verify that the proposed weighted cooperative spectrum sensing framework significantly improves the sensing accuracy.     

An architectural framework for CKBS applications

This paper presents an architectural framework for cooperating knowledge based systems (CKBSs) with parallels drawn from the multiagent systems of DAI. A CKBS is distinguished from a multiagent system by its need to provide a workable approach for real-world distributed applications. The framework proposed considers only interagent activities in what is called transaction-oriented processing. The framework, based largely on well-tested computer science concepts, provides for a multilayered edifice with information transparency, and a multilevel schema to suit different user expertise. It permits dynamic definition of cooperation strategies for different tasks as required, in a high-level language providing relative ease of use. A particular novelty is the interpretation of actions as side-effects of update operations on action tuples transmitted among agents via what are called shadows. This provides the generality needed. Effectiveness, flexibility, and ease of use are some of the key considerations     

Partial identities as a foundation for trust and reputation

This paper explores the relationships between the hard security concepts of identity and privacy on the one hand, and the soft security concepts of trust and reputation on the other hand. We specifically focus on two vulnerabilities that current trust and reputation systems have: the change of identity and multiple identities problems. As a result, we provide a privacy preserving solution to these vulnerabilities which integrates the explored relationships among identity, privacy, trust and reputation. We also provide a prototype of our solution to these vulnerabilities and an application scenario.     

Explanation-aware service selection: rationale and reputation

A collection of 1 billion¹ publicly available web services can form an internet-scale infrastructure for building diverse applications. For a given application, selection of services and service providers from this collection becomes important and reputation is recognized as a key factor for this purpose. However, current reputation systems are limited in their ability to exchange reputation information between heterogeneous systems. To facilitate meaningful exchange and reuse of reputation information and for the overall determination of reputation, we identify the need to infer and explicate rationale for ratings. We present our knowledge-based approach to inferring and explicating rationale for ratings. We show that this approach facilitates detection of deception and collusion, user preference elicitation, explication of rationale behind user ratings and generation of personalized service recommendations.     

On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

Abstract

Recently, the SWEET sensing setup has been proposed as a way of exploiting reservoir computing for sensing. The setup features three components: an input signal (the drive), the environment and a reservoir, where the reservoir and the environment are treated as one dynamical system, a super-reservoir. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. This information can be inferred (decoded) by analysing the reservoir state. The decoding is done by using an external drive signal. This signal is optimised to achieve a separation in the space of the reservoir states: Under different environmental conditions, the reservoir should visit distinct regions of the configuration space. We examined this approach theoretically by using an environment-sensitive memristor as a reservoir, where the memristance is the state variable. The goal has been to identify a suitable drive that can achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study. For simplicity reasons, only two environmental conditions were considered (describing a static and a varying environment). A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem. Under both drives the memristance is driven to two different regions of the one-dimensional state space under the influence of the two environmental conditions, which can be used to infer about the environment. The separation occurs if there is a synchronisation between the drive and the environmental signals. To quantify the magnitude of the separation, we introduced a quality of sensing index: The ability to sense depends critically on the synchronisation between the drive and environmental conditions. If this synchronisation is not maintained the quality of sensing deteriorates.     

Design and implementation of a participatory ergonomics program for machine sewing tasks

A participatory ergonomics program was designed and implemented to control and reduce workers' compensation costs within an automobile products manufacturing company. Pareto analysis identified a substantial number of musculoskeletal disorders (MSD) associated with manual, machine sewing of canvas automobile accessory products. This case study will discuss the elements of the participatory ergonomics program, describe its implementation, highlight intervention measures, and present program results. Specifically, during the period of 1993 to 1996, the participatory ergonomics program played a significant part in reducing the number of MSD claims by approximately 85%, and contributed to an overall reduction in workers' compensation incurred loss costs by approximately 42%.     

An exploratory study of buyers’ participation intentions in reputation systems: The relationship quality perspective

Drawing upon a relationship quality framework, this study identifies how satisfaction, trust and different styles of handling conflict influence online buyers’ participation intentions in reputation systems associated with a C2C online shopping platform. Furthermore, we investigate how these effects are moderated by social conformity and the perceived value of knowledge. The results of a survey of 269 online buyers indicate that satisfaction, trust and an accommodating conflict handling style positively impact the intention to submit positive ratings, whereas an avoiding conflict handling style negatively affects the intention to submit positive ratings. Implications and suggestions for future research are provided.     

Universal product design involving elderly users: a participatory design model

Recent studies have shown that people prefer to age in their familiar environments, thus guiding designers to provide a safe and functionally appropriate environment for ageing people, regardless of their physical conditions or limitations. Therefore, a participatory design model is proposed where human beings can improve their quality of life by promoting independence, as well as safety, useability and attractiveness of the residence. Brainstorming, scenario building, unstructured interviews, sketching and videotaping are used as techniques in the participatory design sessions. Quality deployment matrices are employed to find the relationships between the elderly user's requirements and design specifications. A case study was devised to apply and test the conceptual model phase of the proposed model.     

A reciprocal framework for spatial K-anonymity

Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. The main idea of SKA is to replace the exact location of a user U with an anonymizing spatial region (ASR) that contains at least K−1 other users, so that an attacker can pinpoint U with probability at most 1/K. Simply generating an ASR that includes K users does not guarantee SKA. Previous work defined the reciprocity property as a sufficient condition for SKA. However, the only existing reciprocal method, Hilbert Cloak, relies on a specialized data structure. In contrast, we propose a general framework for implementing reciprocal algorithms using any existing spatial index on the user locations. We discuss ASR construction methods with different tradeoffs on effectiveness (i.e., ASR size) and efficiency (i.e., construction cost). Then, we present case studies of applying our framework on top of two popular spatial indices (namely, R*-trees and Quad-trees). Finally, we consider the case where the attacker knows the query patterns of each user. The experimental results verify that our methods outperform Hilbert Cloak. Moreover, since we employ general-purpose spatial indices, the proposed system is not limited to anonymization, but supports conventional spatial queries as well.